Large-scale Logo Detection

Abstract

Logo detection is crucial for trademark compliance and media monitoring, enabling companies to monitor online trademark usage and evaluate brand visibility on social media and advertisements. The use of large datasets significantly improves accuracy and generalization, emphasizing the need for high-quality datasets to optimize performance and enhance reasoning abilities in visual detection models. This drove us to create Logo4500, an unparalleled dataset featuring 4,500 logo categories and over 293,000 meticulously labeled images. To ensure the dataset's quality, we meticulously designed the construction and annotation process, with detailed information provided in our paper. Compared to existing logo datasets, Logo4500 offers greater diversity and class imbalance, making it more reflective of real-world distribution. Leveraging this high-quality dataset, we introduce a benchmark called Frequency-Aware Learnable Dual Reweighting Network (FALDR-Net), which enhances the representation of ambiguous features and addresses class imbalance for large-scale logo detection. We conducted extensive experiments, evaluating various recent methods on this new dataset and several existing publicly available logo datasets, demonstrating its effectiveness. Additionally, we verified Logo4500's generalization ability in several tasks. We anticipate that Logo4500 and the benchmark will inspire further exploration in the logo-related research community, facilitating the advancement of visual foundation models.